Methods, systems and computer programs for assessing CHD risk using adjusted HDL particle number measurements

ABSTRACT

Methods, computer program products and apparatus determine a subject&#39;s risk of having or developing CHD using a calculated HDL particle risk number and/or a mathematical model of risk associated with HDL particles that adjusts concentrations of at least one of the subclasses of small, medium and large HDL particle measurements to reflect predicted CHD risk. A calculated LDL particle risk number may also be generated as well as a lipoprotein particle index derived from the ratio of R LDL /R HDL .

RELATED APPLICATIONS

This application claims the benefit of priority of U.S. ProvisionalPatent Application Ser. No. 60/746,894 filed May 10, 2006, the contentsof which are hereby incorporated by reference as if recited in fullherein.

FIELD OF THE INVENTION

The present invention relates generally to analysis of lipoproteins. Theinvention may be particularly suitable for NMR analysis of lipoproteinconstituents in blood plasma and serum.

BACKGROUND OF THE INVENTION

In the past, “advanced” lipoprotein test panels have typically includeda lipoprotein measurement of average low-density lipoprotein (LDL)particle size as well as LDL particle number, the latter representingthe concentration or quantity (in concentration units such as nmol/L)and the former representing the average size of the LDL particles (in nmunits) making up the LDL in the sample. For example, in the NMRLipoProfile® lipoprotein panel report available from LipoScience, Inc.,located in Raleigh, N.C., the average LDL particle size corresponds tothe average size of a sample's total LDL particles, i.e., the averagesize of the combined small, intermediate and large LDL particles. Anyone person can have LDL particles present in a continuum of differentparticle sizes. See www.liposcience.com and U.S. Pat. No. 6,576,471 forexemplary reports of particular lipoprotein subclass parameters, thecontents of the patent are hereby incorporated by reference as ifrecited in full herein.

Generally stated, U.S. Pat. No. 4,933,844, entitled Measurement of BloodLipoprotein Constituents by Analysis of Data Acquired from an NMRSpectrometer to Otvos and U.S. Pat. No. 5,343,389, entitled Method andApparatus for Measuring Classes and Subclasses of Lipoproteins, also toOtvos, describe NMR evaluation techniques that concurrently obtain andmeasure a plurality of different lipoprotein constituents in an in vitroblood plasma or serum sample. See also, U.S. Pat. No. 6,617,167,entitled Method Of Determining Presence And Concentration Of LipoproteinX In Blood Plasma And Serum. The contents of all the above patents arehereby incorporated by reference as if recited in full herein. Toevaluate the lipoproteins in a blood plasma and/or serum sample, theamplitudes of a plurality of NMR spectroscopy derived signals within achemical shift region of the NMR spectrum are derived by deconvolutionof the composite signal or spectrum and are compared to predeterminedtest criteria to evaluate a patient's risk of having or developingcoronary artery or heart disease.

Conventionally, a patient's overall risk of coronary heart disease (CHD)and/or coronary artery disease (CAD), has been assessed based onmeasurements of cholesterol content of a patient's LDL and HDL particles(LDL-C, HDL-C) rather than the numbers of these particles. These tworisk factors are used to assess a patient's risk, and treatmentdecisions may be made to reduce the “bad” cholesterol (LDL-C) orincrease the “good” cholesterol (HDL-C). A convenient combined riskfactor is the ratio of LDL-C/HDL-C (or more commonly used is the ratioof Total Cholesterol/HDL-C, which is almost the same thing)—to give anoverall assessment of risk based on the relative amounts of LDL and HDL.Many physicians like the simplicity offered by the ratio and it is oftenused to gauge the success of LDL and/or HDL treatment interventions. Ifonly the ratio is reported, however, the doctor won't know whether todirect therapy to reduce the numerator (LDL) or increase the denominator(HDL). So LDL-C and HDL-C plus the ratio are generally reported.Unfortunately, HDL-C does not adequately reflect the numbers of HDLsubclass particles and may not be representative of a person's trueHDL-related risk of having or developing CHD. In view of the foregoing,there remains a need to provide improved predictive models for assessinga person's risk of developing or having CHD.

SUMMARY

Certain embodiments of the present invention are directed at providingmethods, systems, and computer program products with at least oneadjusted measure of HDL particles of a discrete size range taken from ablood plasma or serum sample that may provide a better and/or easier tounderstand risk number to facilitate patient risk stratification andenable more effective treatment decisions compared with use ofconventional markers of LDL-based risk alone, with HDL cholesterol(HDL-C) or with the ratio of LDL-C/HDL-C.

The adjusted measures may employ a mathematical model that can provide a“Good Particle Index”. Adjusted measures of LDL subclass particles mayalso be generated to provide a “Bad Particle Index”. A ratio of theBad/Good particle indexes may also be generated as a LipoproteinParticle Index and reported as an alternative to TC/HDL-C or LDL-C/HDL-Cratios to improve and/or increase the predictive power of a CHD riskanalysis over a population. The predictive risk assessment number and/ormodels may be particularly useful for both automated screening for CHDrisk and making more effective therapeutic management decisions to lowerthe risk of the patient for CHD.

Embodiments of the invention are directed to methods of determining asubject's risk of having and/or developing CHD. The methods include: (a)obtaining concentration measurements of small and large HDL subclassparticles in a blood plasma or serum sample; (b) programmaticallyadjusting at least one of the small and large HDL subclass particlemeasurement values; and (c) determining a subject's risk of havingand/or developing CHD based on the at least one adjusted HDL subclassparticle measurement number.

Some embodiments of the invention are directed to methods fordetermining a subject's risk of CHD that include: (a) obtaining NMRderived concentration measurements of small and large HDL subclassparticles in a biosample of interest; (b) applying a weighting factor toat least one of the measured large and small HDL particleconcentrations; and (c) calculating an HDL risk predictor number usingthe weighted HDL particle concentration(s).

Other embodiments are directed to computer program products foradjusting measured in vitro concentrations of HDL particles to assessCHD risk. The computer program product includes a computer readablestorage medium having computer readable program code embodied in themedium. The computer-readable program code includes computer readableprogram code that adjusts measured in vitro concentrations of at leastone of small and large HDL particle subclasses to generate an HDL risknumber to reflect a subject's risk of having or developing CHD.

Still other embodiments are directed to a system for obtaining dataregarding lipoprotein constituents in a subject. The system includes:(a) an NMR spectrometer for acquiring at least one NMR spectrum of an invitro blood plasma or serum sample; and (b) a controller incommunication with the NMR spectrometer, the controller comprising acomputer readable storage medium having computer readable program codeembodied in the medium. The computer-readable program code includes: (i)computer program code for determining concentrations of small and largeHDL particle subclasses in the sample undergoing analysis; and (ii)computer program code for adjusting at least one of the determined smalland large HDL particle concentrations to determine a risk of developingor having CHD.

The method may optionally include calculating a LDL lipoprotein riskparameter number using at least two of small, medium and large LDLparticle concentration values where the concentration values areincreased relative to the measured small LDL particle concentration.

In particular embodiments, the method may include calculating alipoprotein particle index from a ratio of the LDL risk parameter valueto the HDL risk parameter value and may also include generating apatient-specific report presenting the LDL risk parameter value and theHDL risk parameter value along with a patient's risk of CHD based on thepresented values.

Other embodiments are directed to methods of assessing CHD risk in apatient. The methods include: generating a single risk predictorvariable using a ratio of a weighted LDL particle number as a numeratorand a weighted HDL particle number as a denominator. The method can beimplemented as a computer program product.

As will be appreciated by those of skill in the art in light of thepresent disclosure, embodiments of the present invention may includemethods, systems, apparatus and/or computer program products orcombinations thereof.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a graph showing the chemical shift spectra of a representativesample of lipoprotein constituent subclasses.

FIG. 2 is a schematic illustration of the dissimilar HDL subclassparticle compositions of two patients, both of whom have the same totalnumber of HDL particles but dissimilar HDL-based risk of CHD asrecognized according to embodiments of the present invention.

FIG. 3 is an exemplary report that uses a HDL Risk number and mayoptionally use a HDL Risk number to reflect a subject's HDL-based riskaccording to embodiments of the present invention.

FIG. 4 is a graph illustrating NMR spectra for a composite plasma sampleand the lipoprotein subclass and protein components thereof, with thepeaks for methyl groups being illustrated.

FIG. 5 is a block diagram of operations that can be used to evaluatesignal data according to embodiments of the present invention.

FIG. 6 is a block diagram of operations that can be used to evaluatesignal data according to embodiments of the present invention.

FIG. 7 is a schematic diagram of an interrogation protocol used toevaluate signal data for composite spectra having contributions fromoverlapping constituents according to embodiments of the presentinvention.

FIG. 8 is a schematic diagram of a data processing system according toembodiments of the present invention.

FIG. 9 is a schematic illustration of a NMR spectroscopy apparatusaccording to embodiments of the present invention.

The foregoing and other objects and aspects of the present invention areexplained in detail in the specification set forth below.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

The present invention now is described more fully hereinafter withreference to the accompanying drawings, in which embodiments of theinvention are shown. This invention may, however, be embodied in manydifferent forms and should not be construed as limited to theembodiments set forth herein; rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art.

Like numbers refer to like elements throughout. In the figures, thethickness of certain lines, layers, components, elements or features maybe exaggerated for clarity. Broken lines illustrate optional features oroperations unless specified otherwise.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items. As used herein, phrases such as “between X and Y” and“between about X and Y” should be interpreted to include X and Y. Asused herein, phrases such as “between about X and Y” mean “between aboutX and about Y.” As used herein, phrases such as “from about X to Y” mean“from about X to about Y.”

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this invention belongs. It will befurther understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the specification andrelevant art and should not be interpreted in an idealized or overlyformal sense unless expressly so defined herein. Well-known functions orconstructions may not be described in detail for brevity and/or clarity.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, components, regions, layersand/or sections, these elements, components, regions, layers and/orsections should not be limited by these terms. These terms are only usedto distinguish one element, component, region, layer or section fromanother region, layer or section. Thus, a first element, component,region, layer or section discussed below could be termed a secondelement, component, region, layer or section without departing from theteachings of the present invention. The sequence of operations (orsteps) is not limited to the order presented in the claims or figuresunless specifically indicated otherwise.

The term “programmatically” means carried out using computer programdirected operations. The terms “automated” and “automatic” means thatthe operations can be carried out with minimal or no manual labor orinput. The term “semi-automated” refers to allowing operators some inputor activation, but the calculations and signal acquisition as well asthe calculation of the concentrations of the ionized constituent(s) isdone electronically, typically programmatically, without requiringmanual input.

Embodiments of the invention recognize that CHD risk is contributed toindependently by high numbers of “bad” LDL particles and low numbers of“good” HDL particles. LDL particles can create atherosclerosis byentering the artery wall, becoming oxidized, and then being ingested bymacrophages to create cholesterol-rich foam cells, which grow into theatherosclerotic plaque. HDL particles can enter the artery wall andprevent or reverse this process by 1) inhibiting the oxidation of LDLparticles and 2) removing cholesterol from the foam cells and deliveringit back to the liver—a process called reverse cholesterol transport. Theoverall risk of CHD depends on the balance between the bad and goodparticles.

As is generally accepted, HDL-cholesterol and/or LDL-cholesterol levelsprovided by conventional lipid panels fail to sufficiently differentiatepopulations with and without CHD or CAD. As is known to those of skillin the art, the Framingham study proposed a relatively lengthy riskmodel that considers many factors such as age, gender, smoking habits,as well as cholesterol values. The research conducted in the FraminghamOffspring Study also defined normative and at-risk population valuesfrom subjects in the study. See Wilson et al., Impact of NationalGuidelines for Cholesterol Risk Factor Screening The FraminghamOffspring Study, JAMA, 1989; 262: 41-44.

Unfortunately, many patients and clinicians still refer to totalcholesterol and/or LDL-C and HDL-C to define a risk of developing CADand/or to determine whether to begin or alter a therapeutic treatment.Thus, a simple, recognizable, easy- to-use more reliable risk factor mayfacilitate treatment for at-risk patients currently going undetected.

The present invention recognizes that lipoprotein particle physiologyand/or properties can provide a better indicator of atherogenicityimplicit to the lipoproteins that carry cholesterol. Because it is thenumber and size of lipoproteins that determine one's risk of heartdisease (not one's cholesterol levels), drug therapy is typicallytargeted to reduce the number of LDL particles and/or increase thenumber of HDL particles. Embodiments of the present invention aredirected to providing easy to recognize risk numbers that may facilitatetreatment and follow-up that a patient and a clinician can use to morereliably assess risk relative to the cholesterol risk factors commonlyused. More aggressive treatments may be desired when certain LDLparticle subclasses are present in borderline and/or increased amountsand/or when certain HDL particle subclasses are present in borderlineand/or decreased amounts relative to the general population or aclinical baseline.

It is contemplated that, just as LDL-C does not adequately reflect thenumbers of different LDL subclass particles and take into account theirdifferential atherogenicities, HDL-C does not adequately reflect thenumbers of HDL subclass particles, not all of which are equallyanti-atherogenic (i.e., conferring different degrees of protection fromatherosclerosis). While not wishing to be bound to any one theory, it iscontemplated that some HDL subclass particles may be better antioxidantsor more effective mediators of reverse cholesterol transport. Thus,embodiments of the present invention weight the HDL subclasses andcombine the values in a HDL risk parameter that may provide better riskprediction than that given by total HDL-P or HDL-C. Also, embodiments ofthe present invention weight the LDL subclasses and combine the valuesin a LDL risk parameter that may provide better risk prediction thanthat given by total LDL-P or LDL-C.

It is currently believed that exemplary numbers that patients would havefor these weighted risk parameters may be:

(a) the weighted R_(HDL) numbers would range from about 10-80 μmol/L:

(b) the R_(LDL) range would be about 500-3,000 nmol/L; and

(c) the ratio lipoprotein particle risk parameter (R_(LDL)/R_(IDL)) mayvary from about 6 to 300.

Different therapies that increase HDL-C by the same amount may notincrease the HDL subclasses proportionately. Some drugs, for example,increase HDL-C mainly by increasing the number of small HDL particles(such as those in the fibrate class). Others increase mainly largeHDL-P. The weighted HDL index will change differentially with differenttherapies, indicating greater or lesser clinical benefit and may provideincreased clinical data for evaluating therapeutic efficacy.

Presently, the LDL particle sizes are characterized as Pattern A (large)and Pattern B (small). Pattern A can be defined as large averageparticle sizes which typically includes sizes of between about 20.5-23.0nm. Pattern B can be defined as smaller average particle sizes betweenabout 18.0-20.5 nm.

As used herein, the term “small LDL particles” can include particleswhose sizes range from between about 18.0 to about 21.2 nm.Alternatively, they can include particles in the very small (betweenabout 18.0-19.8 nm) and intermediate small (between about 19.8-21.2 nm)diameter ranges. The term “large LDL particles” can include particlesranging in diameter between about 21.2-23.0 nm. Intermediate sized smallparticles may be parsed into one of the small and/or large designationsor be measured separately as including particles in a size range that istypically near about 20.5 nm. It is noted that the LDL subclasses ofparticles can be divided in other size ranges. For example, small may bebetween about 18.0-20.5 nm, intermediate may be between about 20.5-21.2nm, and large may be between about 21.2-23 nm. In addition,intermediate-density lipoprotein particles (“IDL” or “IDL-P”), whichrange in diameter from approximately 23.0-27.0 nm, can be included amongthe particles defined as LDL.

As used herein, the term “HDL subclasses” refers to size groupings ofHDL particles. The predictive mathematical (HDL subclass risk) model canbe used with NMR signal measurement methods that measure lipoproteinconstituents using signals having spectral contribution from chemicalconstituents having overlapping signals. In certain embodiments, the HDLsubclass size ranges may be further defined as three, or even more,different discrete and measurable constituents (i.e., H1, H2, H3, H4)and each may be individually weighted according to the mathematicalmodel.

The term “large HDL particles” (“large HDL-P”) can include HDLsubclasses of particles whose sizes range from between about 8.8 toabout 13 nm. The term “small HDL particles” (small HDL-P) can includeparticles ranging in diameter between about 7.3 to about 8.2 nm. Theintermediate or medium HDL particles (medium HDL-P) can be parsed intoone of the small or large designations or be measured separately asincluding particles in the size range that is typically between about8.2 to 8.8 nm.

The terms CAD and CHD are used interchangeably to correspond to apatient or subject's risk of developing or having coronary artery and/orcoronary heart disease, respectively.

The terms “population norm” and “standard” value associated with alipoprotein measurement can be the values defined by the FraminghamOffspring Study discussed below. However, the instant invention is notlimited to these population values as the presently defined normal andat-risk population values for LDL particle concentrations or levels maychange over time.

The flowcharts and block diagrams of certain of the figures hereinillustrate the architecture, functionality, and operation of possibleimplementations of analysis models and evaluation systems and/orprograms according to the present invention. In this regard, each blockin the flow charts or block diagrams represents a module, segment,operation, or portion of code, which comprises one or more executableinstructions for implementing the specified logical function(s). Itshould also be noted that in some alternative implementations, thefunctions noted in the blocks might occur out of the order noted in thefigures. For example, two blocks shown in succession may in fact beexecuted substantially concurrently or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved.

LDL is known to carry the so-called “bad” cholesterol. LDL particlescome in different sizes. Conventionally, the smaller sizes have beenthought to be the most dangerous type in that they were generallythought to be inherently more atherogenic than large particles. See,Sacks et al., Clinical review 163. Cardiovascular endocrinology: Lowdensity lipoprotein size and cardiovascular disease: a reappraisal, J.Clin. Endocrinol Metab., 2003; 88: 4525-4532. Typical past studiesexamined only the distribution of LDL subclasses or LDL size phenotype(large or small) rather than particle concentrations of LDL subclasses.However, some studies have suggested that large LDL size may beassociated with CHD. See, Campos et al., Predominance of large LDL andreduced HDL2 cholesterol in normolipidemic men with coronary heartdisease, Arterioscler Thromb Vasc Biol., 1995; 15: 1043-1048; and Camposet al., Low-density lipoprotein size, pravastatin treatment, andcoronary events, JAMA, 2001; 286, 1468-1474. Indeed, it is known thatlarge LDL predominates in patients with familial hypercholesterolemiaand those consuming high saturated fat diets. See, Patsch et al.,Characterization of lipoprotein in a kindred with familialhypercholesterolemia, J. Lipid Res. 1982; 23:1196-1205; and Dreon etal., Change in dietary saturated fat intake is correlated with change inmass of large low-density-lipoprotein particles in men, Am. J. Clin Nutr1998; 67: 828-836.

Despite the above, it is believed that, in the past, risk associatedwith small and large LDL particles was not compared on a per particlebasis with control for the inverse correlation between small and largeLDL particles. Also, the risk associated with small and large LDLparticles was confounded due to their differing association with otherlipoproteins and traditional cardiovascular risk factors. See e.g., Moraet al., Both Large and Small LDL Particle Concentrations areIndependently Associated with Carotid Atherosclerosis in theMulti-Ethnic Study of Atherosclerosis (MESA), Abstract presented at 2005Scientific Sessions of the American Heart Association, Dallas, Tex.,Circulation. 2005; 112: II-802. See also, Rosenson et al., Relations oflipoprotein subclass levels and low density lipoprotein size toprogression of coronary artery disease in the Pravastatin Limitation ofAtherosclerosis in the Coronary Arteries (PLA C-1) trial, Am J.Cardiol., 2002; 90:89-94. Embodiments of the instant invention weightLDL particles to assess risk recognizing that both large and small LDLsubclasses are associated with atherosclerosis with insignificant (orno) additional contribution of LDL-C once the inverse correlationbetween the two subclasses is taken into account.

Not wanting to be limited to any one theory, it is contemplated that, ona per particle basis, large LDL particles (large LDL-p) can beassociated with a greater amount of carotid atherosclerosis than smallLDL particles (small LDL-p) and that small and large LDL aresignificantly associated with atherosclerosis independent of other riskfactors. Carotid atherosclerosis measured non-invasively by ultrasoundis closely related to all major cardiovascular risk factors andgenerally accepted to be a strong predictor of clinical cardiovasculardisease. See, e.g., O'Leary et al., Intima-media thickness; a tool foratherosclerosis imaging and event prediction, Am. J. Cardiol., 2002; 90:18L-21L.

The amount of cholesterol per LDL and HDL particle varies widely fromperson to person. One reason is that large LDL particles have highercholesterol content than small LDL particles. But even among people withexactly the same numbers of small and large LDL particles, LDLcholesterol levels vary because of differences in the relative amountsof cholesterol and triglycerides inside the particles. As a consequence,LDL cholesterol levels are an imperfect surrogate measure of a patient'sLDL particle numbers and the CHD risk that these particles confer. See,e.g., Cromwell et al., Low-density lipoprotein particle number and riskfor cardiovascular disease, Curr. Atheroscler. Rep., 2004; 6:381-387.

The present invention recognizes that large and small LDL particles donot confer exactly the same CHD risk and large and small HDL particlesdo not confer the same CHD protection. As a result, an improvement inrisk prediction may be realized by employing at least one weightingfactor that adjusts the measurement of one or more different HDLparticle subclasses. In some embodiments, the risk assessment may alsooptionally employ at least one weighting factor to adjust themeasurement of one or more different LDL particle subclasses to accountfor their different contributions to atherosclerosis.

As an example, as shown in FIG. 2, consider two patients with the samenumber of total HDL particles (HDL-P). Patient A has a smaller number oflarge HDL particles and more small HDL particles than Patient B. Iflarge HDL particles, on a per particle basis, provide greateranti-atherogenic protection (associated with a lesser CHD risk), thenthe risk of Patient A would be greater than that of Patient B.

Similarly, consider two patients with the same number of LDL particles(LDL-P). Patient A has a greater number of large LDL particles and fewersmall LDL particles than Patient B. If large LDL particles, on a perparticle basis, confer greater CHD risk, then the risk of Patient Awould be greater than that of Patient B.

Table 1 compares prophetic carotid atherosclerosis data (as measured bycarotid ultrasound and expressed as the increase in intima mediathickness “IMT”, associated with a given increment in concentration oflarge or small LDL) to illustrate that it can take more (shown as morethan twice as many) small LDL particles to cause the same increase inatherosclerosis as are caused by a lesser number of large LDL particles.As shown in this particular example, large LDL particles can be more(such as about 1.5-2.5 times more) atherogenic per particle than smallLDL particles. This data is believed to be generally representative offindings in a study carried out using data from patients enrolled in theMulti-Ethnic Study of Atherosclerosis (MESA). See the Examples Sectionhereinbelow.

TABLE 1 LDL Particle Risk Type Increment Increase in IMT Large LDLparticles (nmol/L) 220 40 microns Small LDL particles (nmol/L) 450 40microns

While not wishing to be bound by any one theory, it is possible that invivo a relatively steady-state concentration of small LDL particles maytravel from the blood stream past the endothelial cells and into thearterial intima due to their smaller size and/or cellular make-up, butthat large LDL particles carry more atherogenic material, so a depositof lesser numbers of large LDL particles can be problematic as well.Thus, a weighting factor applied to measures of large and/or small LDLparticles in blood plasma and/or serum samples can provide a riskindicator of CAD risk.

In the past, an average LDL particle size of 20.5 nm might result from asample having no large and no small LDL particles (all intermediate) aswell as a sample having 50% large and 50% small (averaging to anintermediate particle size). Hence, for a similar total LDL particlenumber, the CHD-risk for each of these samples may be different, butpreviously perhaps not clearly stated or easily recognized.

Certain embodiments of the present invention are directed at providingmethods, systems, and computer program products that use HDL risknumbers (or mathematical models) that employ adjusted values of discretemeasures of concentration of HDL (HDL subclasses of different(predetermined) particle size ranges) that may simplify, improve and/orincrease the predictive power of a risk analysis over a population. Themodels may be particularly useful in automated screening tests and/orrisk assessment evaluations for CAD screening of in vitro biosamples.

In certain embodiments, a predetermined mathematical HDL subclass riskmodel can be configured to evaluate each measured amount of target(predetermined) HDL subclass, then determine the value of a predictorvariable based on adjusted measurements of at least one differentsubclass (size range) of HDL particles. Embodiments of the invention cananalyze samples to provide discrete concentration measurements for bothsmall HDL particles and large HLDL particles, recognizing that each sizecategory has some degree of anti-atherogenicity, and then calculate apredictor value (Risk_(HDL)) considering each HDL subclass measurement.

Certain embodiments of the present invention can also use LDL risknumbers (or mathematical models) that employ adjusted values of discretemeasures of concentration of LDL (LDL subclasses of different(predetermined) particle size ranges) that may simplify, improve and/orincrease the predictive power of a risk analysis over a population.

In particular embodiments, an HDL particle risk predictor index ornumber (Risk_(HDL)) and an LDL particle risk predictor index or number(Risk_(LDL)) can be calculated by multiplying a weighting factor to atleast one HDL subclass and at least one LDL subclass measurement. FIG. 3illustrates an exemplary patient test report of R_(HDL) and R_(LDL) aswell as an overall risk index of the ratio of the risk parameters,R_(LDL)/_(HDL) each of which can be used as a CHD risk assessment guide.

For the HDL risk index, the weighting can be applied to either or boththe amount of small HDL particles (HDL_(S)) and/or to the large HDLparticles (HDL_(L)) measured in the sample. If the weighting factor isonly applied to the HDL_(S) concentration, then it is presentlycontemplated that the weighting factor should be less than one, such asbetween about 0.25-0.75, and may be between about 0.5-0.7. If theweighting factor is applied only to the HDL_(L) measurement, then theweighting factor may be between about 1.1 to about 3, and may be about2.2+/−0.3. Exemplarily weights based on per particle measurements areprovided below in Table 4. Optimal weighting factors may be confirmedwith data from further studies.

Similarly, for the LDL risk index, the weighting can be applied toeither or both the amount of small LDL particles (LDL_(S)) and/or to thelarge LDL particles (LDL_(L)) measured in the sample. If the weightingfactor is only applied to the LDL_(S) concentration, then it ispresently contemplated that the weighting factor should be less thanone, such as between about 0.25-0.75, and may be between about 0.5-0.7.If the weighting factor is applied only to the LDL_(L) measurement, thenthe weighting factor may be between about 1.1 to about 2, and may beabout 1.5+/−0.3. Exemplarily weights based on per particle measurementsare provided below in Tables 2 and 3. Optimal weighting factors may beconfirmed with data from further studies.

This LDL risk model may be expressed using the following mathematicalequation:X(LDL _(S))+Y(LDL _(L))=Risk_(LDL.)(e.g., the bad particle index)

The HDL risk model may be expressed using the following mathematicalequation:X′(HDL _(S))+Y′(HDL _(L))=Risk_(HDL)(e.g., the good particle index)

where X, X′ or Y, Y′ can be one, typically X, X′=1 and Y,Y′>1, and/orwhere Y, Y′>X, X′. In some embodiments, both weighting factors X, X′ andY, Y′ may be above 1.

In other embodiments, a third weighting factor “Z” can be used to addintermediate LDL particles and/or IDL particles to the LDL risk modeland medium HDL particles (HDL_(M)) to the HDL risk model.Z(IDL-P)+X(LDL _(S))+Y(LDL _(L))=RiSk_(LDL)Z′(HDL _(M))+X′(HDL _(S))+Y′(HDL _(L))=RiSk_(HDL)

For LDL, the third weighting factor Z may be greater than that of X andY, and may have a value that is increased between about 4-6 above LDLs(relative to measurement of the small LDL particles). Hence, in certainembodiments: Z>Y>X. For HDL, the third weighting factor Z′ may be lessthan Y′ and greater than X′, such as a value that is about 30-50%greater than X′ but about 30-50% less than Y′. The X′, Y′ and/or Z′values used for R_(HDL) are typically different from X, Y, Z used forR_(LDL). Additional weights and particle subclass subdivisions may beused. As an alternative to linear models primarily discussed herein, amulti-factorial (non-linear) model can be used to automaticallycalculate a risk number using an adjusted LDL or HDL subclassmeasurement obtained electronically based on a measurement of abiosample without requiring clinician input on non-automaticallymeasured parameters (such as BMI, smoking habits and the like). The HDLor LDL subclass measurements can be combined and adjusted toautomatically generate the HDL or LDL particle risk number and/orweighted risk index, which can be electronically tracked over time.

In certain embodiments, X can be weighted above 1 and Y can be weightedabove 1, to reflect the measured concentrations of the LDL particles'contribution to risk. In some embodiments, X can be weighted with aweighting factor that is below 1 and Y can be at about 1 or greaterthan 1. In other embodiments, X and Y can have weighting factors thatare below 1, with Y being greater than X for certain or all measures oflarge LDL particle concentrations. In some embodiments, the subclassmeasurements can be adjusted so that the large LDL subclass measurementreflects between a 1.5-3 fold multiplier over the small LDL subclassmeasurement.

In particular embodiments, Y can be selected to increase the measuredvalue of large LDL particles by at least about 25% relative to the smallLDL particle measurement.

The weighting factors X, X′, Y, Y′ (and/or Z, Z′) in the LDL and/or HDLrisk model, respectively, may be constants applied across substantiallyall samples. In other embodiments, the weighted values may be defined insitu and/or applied using a formula or a programmatically implemented ordirected look-up table, based on a particular sample's contents. Forexample, X and Y and/or X′, Y′ may vary depending on age, gender, orother patient factor and/or based on the total number or HDL or LDLparticles present, and/or the amount of each subclass particle measuredrelative to the general population, and/or as a percentage of theparticles. For example, where large LDL subclass particles are presentin an amount greater than the median value of the general population, ahigher than normal weight can be assigned to Y. Where both small LDLparticles and large LDL particles are present in amounts greater thanthe median of the general population, and/or the total LDL particlenumber is borderline or high, Y and/or both X and Y may be assigned agreater value compared to those situations where large and/or small LDLsubclass particle numbers are less than the population median.

In certain embodiments, the model is configured to apply at least onedifferent increased weighting factor, as increased concentrations of thesmall and/or large HDL or LDL particles are determined. Thus, for highlevels of particular HDL or low levels of LDL particle subclassconcentrations, the risk model used to determine the risk number may notapply any weighting factors to the measured values of small and/or largesubclass particle concentrations to arrive at the risk number. However,as the total or individual concentrations of respective subclassparticles exceed a predetermined threshold level, a weighting factor(s)or altered weighting factor can then be applied.

The predictive model can be used with any suitable HDL or LDL subclassmeasurement technique, including, but not limited to, gradient gelelectrophoresis, density gradient ultracentrifugation, and NMRspectroscopy. However, in particular embodiments, the predictive modelmay be used with NMR spectroscopy measurements of HDL and LDL subclassesin in vitro blood plasma and/or serum samples.

In operation, to obtain the value of the predictor risk variablesR_(HDL) and R_(LDL), particle concentration measurements of at leastlarge and small HDL and/or LDL particles in a sample of interest can beobtained (such as in nmol/L units or other suitable metric). If usingNMR spectroscopy, then, similar to conventional techniques forNMR-derived particle concentration measurements, particle concentrations(nanomoles of particles per liter, nmol/L) for small and large HDLand/or LDL subclass particles can be calculated by measuring the signalamplitudes broadcast by these subclasses and applying conversion factorsderived from the NMR measurements of isolated subclass standards ofknown particle concentration. The particle concentrations of the largeand small HDL and/or LDL subclasses can then be adjusted (such as, forexample, multiplied by their weighting factors (X, X′, Y, Y′ etc.) andadded together to provide the value of the R_(HDL) and/or R_(LDL) (alsotypically in nmol/L)).

For example, compare two prophetic measurements of large and small LDLconcentrations in two different patients, each having a total LDLparticle number that is substantially the same, using the X (1.25) and Y(2.5) weighting factors to calculate an associated LDL risk number.

Patient (1)L-LDLp 600 nmol/LS-LDLp 800 nmol/LRisk_(LDL)=[(X)(800)+(Y)(600)]=2500

Patient (2)L-LDLp 400 nmol/LS-LDLp 1000 nmol/LRiSk_(LDL) =[X(1000)+Y(400)]=2250

In other embodiments, the two prophetic measurements of large and smallLDL concentrations in the two different patients can be adjusted usingX=1 and Y=1.5 (making X=1 and adjusting Y so that the weighted large LDLmeasurement can generate the relative increase in risk) to calculate anassociated LDL risk number.

Patient (1)Risk_(LDL)=[(X)(800)+(Y)(600)]=1700

Patient (2)Risk_(LDL) =[X(1000)+Y(400)]=1600

Thus, although each patient has a similar total LDLp number, patient (1)has a higher risk of CAD than patient (2) according to the LDL risknumber. Similar weighted measurements can be used to determineRisk_(HDL)

For user ease of recognition, the risk number can be converted to astraight or scalar risk number, i.e., 1-10, or used as the risk numberitself. For example, patients (1) and (2) can be assigned the risknumber calculated above, or the number may be scaled in a particularway. For example, patient (1) may have a risk index of 8, while patient(2) may have a risk index of 7.

In contrast to previous analysis methods, two people having the same HDLparticle number and LDL particle number may now have a differentadjusted (weighted) HDL or LDL risk number based on a weightedconcentration of one or more of the constituents that make up the HDLparticle number or the LDL particle number, that may more appropriatelyrepresent the “true” HDL and LDL-based risk in the person having, forexample, increased amounts of smaller HDL particles, withoutdisregarding benefit from larger HDL particles and increased amounts oflarger LDL particles without disregarding risk from smaller LDLparticles.

In any event, conventionally, the first step in treating increasednumbers of lipoproteins is identification. Embodiments of the presentinvention provide screening tests and reports that analyze the uniqueproperties of lipoproteins to give complete quantitative and qualitativelipoprotein information. The test report can be configured to containsignificant and unique information about an individual's underlying riskfor CHD including the HDL risk predictor variable, R_(HDL), and the LDLrisk predictor variable, R_(LDL) (i.e., the adjusted HDL and LDLparticle numbers) that can be used to assess the HDL-particle and LDLparticle-related risk in the lipoprotein risk analysis section as shownin FIG. 3. As noted above, particular embodiments of the presentinvention are directed to NMR-derived measurements of lipoproteinssimilar to a NMR LipoProfile® NMR-derived cholesterol or lipoproteinpanel, which includes a R_(HDL) and R_(LDL) number as well as a totalsingle risk number Risk LDL/HDL and may optionally include values forother lipoproteins of interest that may also be considered whenevaluating a patient, including concentrations of subclasses of HDL andsubclasses of VLDL.

Exemplary NMR Sample Analysis

As is known, an NMR lipoprotein subclass analysis can be carried out tomeasure lipoprotein subclass levels and average VLDL, LDL, and HDLparticle diameters by NMR spectroscopy. The NMR method uses thecharacteristic signals broadcast by lipoprotein subclasses of differentsize as the basis of their quantification. See Otvos J D, Jeyarajah E J,Bennett D W, Krauss R M. Development of a proton nuclear magneticresonance spectroscopic method for determining plasma lipoproteinconcentrations and subspecies distributions from a single, rapidmeasurement, Clin Chem 1992; 38:1632-1638; and Otvos J, Measurement oflipoprotein subclass profiles by nuclear magnetic resonancespectroscopy, Clin Lab 2002; 48: 171-180. Each subclass signal emanatesfrom the aggregate number of terminal methyl groups on the lipidscontained within the particle, with the cholesterol esters andtriglycerides in the particle core each contributing three methyl groupsand the phospholipids and unesterified cholesterol in the surface shelleach contributing two methyl groups. The total number of methyl groupscontained within a subclass particle is, to a close approximation,dependent only on the particle's diameter and is substantiallyunaffected by differences in lipid composition arising from such sourcesas variability in the relative amounts of cholesterol ester andtriglyceride in the particle core, varying degrees of unsaturation ofthe lipid fatty acyl chains, or varying phospholipid composition. Forthis reason, the methyl NMR signal emitted by each subclass serves as adirect measure of the particle concentration of that subclass.

In the past, NMR spectra of each plasma specimen (0.25 ml) were acquiredin replicate (typically about 5 separate spectra are acquired) using anautomated 400 MHz lipoprotein analyzer and the lipid methyl signalenvelope decomposed computationally to give the amplitudes of thecontributing signals of 16 lipoprotein subclasses (chylomicrons, 6 VLDL,1 IDL, 3 LDL, 5 HDL). Conversion factors relating these signalamplitudes to subclass concentrations expressed in particleconcentration units or lipid mass concentration units (cholesterol ortriglyceride) were then applied. The conversion factors were derivedfrom NMR and chemical analyses performed on a set of purified subclassstandards of defined size, which were isolated from a diverse group ofnormo- and dyslipidemic individuals using a combination ofultracentrifugation and agarose gel filtration chromatography. Particleconcentrations (in mol/L (nmol of particles per liter)) were calculatedfor each subclass standard by measuring the total concentration of corelipid (cholesterol ester plus triglyceride) and dividing the volumeoccupied by these lipids by the core volume per particle calculated fromknowledge of the particle's diameter. Rifai N, Warnick G R, Dominiczak MH, eds: Handbook of LipoProtein Testing, 2nd Edition, Washington, D.C.,AACC Press; 2000, pp 609-623. Lipid mass concentrations of VLDLsubclasses are given in mg/dL triglyceride units and those of the LDLand HDL subclasses in mg/dL cholesterol units. Summing the relevantsubclass concentrations gives NMR-derived values for total VLDLtriglycerides, LDL cholesterol, and HDL cholesterol.

Conventionally, the 16 measured subclasses have been grouped foranalysis into the following 10 subclass categories (but different sizeranges may also be used as noted above): large VLDL (60-200 nm), mediumVLDL (35-60 nm), small VLDL (27-35 nm), IDL (23-27 nm), large LDL(21.3-23 nm), medium LDL (19.8-21.2 nm), small LDL (18.3-19.7 nm), largeHDL (8.8-13 nm), medium HDL (8.2-8.8 nm), and small HDL (7.3-8.2 nm).IDL and LDL subclass diameters, which are uniformly ˜5 nm smaller thanthose estimated by gradient gel electrophoresis, are consistent withboth electron microscopy and LDL lipid compositional data. See RedgraveT G, Carlson L A, Changes in plasma very low density and low densitylipoprotein content, composition, and size after a fatty meal in normo-and hypertriglyceridemic man. J Lipid Res. 1979; 20:217-29; and Rumsey SC, Galeano N F, Arad Y, Deckelbaum R J. Cryopreservation with sucrosemaintains normal physical and biological properties of human plasma lowdensity lipoproteins, J Lipid Res 1992; 33:1551-1561.

Weighted average VLDL, LDL, and HDL particle sizes (nm diameter) werecomputed as the sum of the diameter of each subclass multiplied by itsrelative mass percentage as estimated from the amplitude of its methylNMR signal. LDL and HDL subclass distributions determined by gradientgel electrophoresis and NMR are highly correlated. Otvos J D,Measurement of lipoprotein subclass profiles by nuclear magneticresonance spectroscopy, Clin Lab 2002; 48:171-180; and McNamara J R,Small D M, Li Z, Schaefer E J, Differences in LDL subspecies involvealterations in lipid composition and conformational changes inapolipoprotein B, J Lipid Res 1996; 37:1924-1935; and Grundy S M, Vega GL, Otvos J D, Rainwater D L, Cohen J C. Hepatic lipase influences highdensity lipoprotein subclass distribution in normotriglyceridemic men:genetic and pharmacological evidence, J Lipid Res 1999; 40:229-234.

Replicate analyses of plasma pools indicate that NMR subclassmeasurements are reproducible, with coefficients of variation <3% forNMR-derived values for total and VLDL triglycerides, LDL and HDLcholesterol, and LDL particle concentration, <4% for VLDL size, and <1%for LDL and HDL average size. Otvos J D, Measurement of lipoproteinsubclass profiles by nuclear magnetic resonance spectroscopy, Clin Lab2002; 48:171-180.

As noted above, the conventional analysis technique described has beenmodified to be able to reliably quantify large and small LDL particleconcentrations as described in the aforementioned co-pending U.S. patentapplication Ser. No. 10/691,103. The evaluation can be further modifiedto implement a predictive model to provide a weighted LDL risk numberaccording to embodiments of the present invention.

An alternative NMR measurement technique is described in Diffusionordered nuclear magnetic resonance spectroscopy: principles andapplications, Prog. In NMR Spec, 34 (1999) 203-256. See also, WO2005/119285 A1, Process of Determination of Lipoproteins in Body Fluid,the contents of which are hereby incorporated by reference as if recitedin full herein.

In addition, as noted above, other evaluation techniques (includingnon-NMR measurement techniques) may also be used according toalternative embodiments of the present invention.

Statistical Operations

In certain embodiments, the methods, systems, and/or computer productsused to evaluate specimens employ statistical fitting models whichevaluate signal data of an unknown sample according to a predeterminedfitting model and standards to identify the presence of at least oneselected chemical constituent and/or to measure the level orconcentration thereof in the sample. More typically, the models,programs, and methods of the present invention are configured toevaluate signal data of a composite sample with highly or closelycorrelated individual constituent spectra (having at least a pluralitywith overlapping signal lines in the spectrum) to identify the presenceof at least 10 different individual constituents and/or the levelthereof. The term “highly” and “closely” are used interchangeably whenused with “correlated” so that in the description that follows either“highly correlated” or “closely correlated” means that a plurality ofconstituents in a sample being analyzed generate respective spectrawhich can overlap in a composite signal that includes spectralcontributions from those constituents.

As will be appreciated by one of skill in the art, the present inventionmay be embodied as an apparatus, a method, data or signal processingsystem, or computer program product. Accordingly, the present inventionmay take the form of an entirely software embodiment, or an embodimentcombining software and hardware aspects. Furthermore, certainembodiments of the present invention may take the form of a computerprogram product on a computer-usable storage medium havingcomputer-usable program code means embodied in the medium. Any suitablecomputer readable medium may be utilized including hard disks, CD-ROMs,optical storage devices, or magnetic storage devices.

The computer-usable or computer-readable medium may be, but is notlimited to, an electronic, magnetic, optical, electromagnetic, infrared,or semiconductor system, apparatus, device, or propagation medium. Morespecific examples (a nonexhaustive list) of the computer-readable mediumwould include the following: an electrical connection having one or morewires, a portable computer diskette, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), an optical fiber, and a portable compact discread-only memory (CD-ROM). Note that the computer-usable orcomputer-readable medium could even be paper or another suitable medium,upon which the program is printed, as the program can be electronicallycaptured, via, for instance, optical scanning of the paper or othermedium, then compiled, interpreted or otherwise processed in a suitablemanner if necessary, and then stored in a computer memory.

Computer program code for carrying out operations of the presentinvention may be written in an object oriented programming language suchas Java®, Smalltalk, Python, Labview, C++, or VisualBasic. However, thecomputer program code for carrying out operations of the presentinvention may also be written in conventional procedural programminglanguages, such as the “C” programming language or even assemblylanguage. The program code may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer or entirely onthe remote computer. In the latter scenario, the remote computer may beconnected to the user's computer through a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

The flowcharts and block diagrams of certain of the figures hereinillustrate the architecture, functionality, and operation of possibleimplementations of analysis models and evaluation systems and/orprograms according to the present invention. In this regard, each blockin the flow charts or block diagrams represents a module, segment,operation, or portion of code, which comprises one or more executableinstructions for implementing the specified logical function(s). Itshould also be noted that in some alternative implementations, thefunctions noted in the blocks might occur out of the order noted in thefigures. For example, two blocks shown in succession may in fact beexecuted substantially concurrently or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved.

The small person-to-person variations in the lineshapes of thelipoprotein classes are caused by the subclass heterogeneity known toexist within each of these lipoprotein classes. FIG. 1 shows thelineshapes and chemical shifts (positions) for a number of subclasses oflipoproteins. As shown in FIG. 1, the chemical shifts and lineshapedifferences between the different subclasses are much smaller than thosebetween the major lipoprotein classes, but are completely reproducible.Thus, differences among the NMR signals from the plasma of individualsare caused by differences in the amplitudes of the lipid resonances fromthe subclasses present in the plasma, which in turn are proportional totheir concentrations in the plasma. This is illustrated in FIG. 4, inwhich the NMR chemical shift spectra of a blood plasma sample is shown.The spectral peak produced by methyl (CH₃) protons 60 (shown as a solidline) is shown for the blood sample in FIG. 4. The spectral peak 61(shown as a dotted line) in FIG. 4 is produced by the arithmetic sum ofthe NMR signals produced by the lipoprotein subclasses of the majorclasses VLDL, LDL, HDL, proteins and chylomicrons, as illustrativelyshown by certain of the subclasses in FIG. 1. It can be seen that thelineshape of the whole plasma spectrum is dependent on the relativeamounts of the lipoprotein subclasses whose amplitudes change (sometimesdramatically) with their relative concentrations in the plasma sample.

Since the observed CH₃ lineshapes of whole plasma samples are closelysimulated by the appropriately weighted sum of lipid signals of itsconstituent lipoprotein classes, it is possible to extract theconcentrations of these constituents present in any sample. This isaccomplished by calculating the weighting factors which give the bestfit between observed blood plasma NMR spectra and the calculated bloodplasma spectra. Generally speaking, the process of NMR lipoproteinanalysis can be carried out by the following steps: (1) acquisition ofan NMR “reference” spectrum for each of the “pure” individual or relatedgroupings of constituent lipoprotein classes and/or subclasses of plasmaof interest, (2) acquisition of a whole plasma NMR spectrum for a sampleusing measurement conditions substantially identical to those used toobtain the reference spectra, and (3) computer deconvolution of theplasma NMR spectrum in terms of the constituent classes and/orsubclasses (or related groupings thereof) to give the concentration ofeach lipoprotein constituent expressed as a multiple of theconcentration of the corresponding lipoprotein reference.

Although the procedure can be carried out on lipoprotein classes,carrying out the process for subclasses of lipoproteins can decrease theerror between the calculated lineshape and the NMR lineshape, thusincreasing the accuracy of the measurement while allowing forsimultaneous determination of the subclass profile of each class.Because the differences in subclass lineshapes and chemical shifts aresmall, it is typically important to correctly align the referencespectrum of each subclass with the plasma spectrum. The alignment ofthese spectra is accomplished by the alignment of control peaks in thespectra, which are known to respond in the same manner to environmentalvariables, such as temperature and sample composition, as do thelipoprotein spectra. One such suitable alignment peak is the peakproduced by CAEDTA, although other EDTA peaks or suitable peak may beutilized. By alignment of the spectra, the small variations in thesubclasses' lineshapes and chemical shifts may be exploited to producehigher accuracy and subclass profiles.

Further description of these methods can be found in U.S. Pat. Nos.4,933,844 and 5,343,389 to Otvos.

Lineshape

The mathematics used in the lineshape fitting process (i.e., leastsquares fit of an unknown function in terms of a weighted sum of knownfunctions) is well known and is described in many textbooks of numericalanalysis, such as F. B. Hildebrand, Introduction to Numerical Analysis,2nd edition, pp. 314-326, 539-567, McGraw-Hill, 1975.

In particular embodiments, reference samples of each constituentlipoprotein and protein component to be analyzed are prepared (typicallythey are refrigerated during storage and allowed to warm prior toanalysis) and placed within the spectrometer 10. An NMR measurement isthen taken on each reference sample to define a standard for therespective constituent. The data for the reference samples (for aplurality of different constituents) is processed and stored in thecomputer 11. Techniques for acquiring and storing NMR spectroscopic dataare well-known to those skilled in this art and need not be described infurther detail. The reference samples or standards may be established apriori and used to measure a plurality of different patient specimens orsamples over time.

To carry out the analysis, the data points of the real part of thesample plasma spectrum that comprise the spectral region to be fit(normally 0.73-0.85 ppm for lipoprotein evaluations) are entered into anarray. This plasma array consists of m discrete data points denotedP_(i) ^(o), i=1,2, . . . m. The data points of the real part of thelipoprotein subspecies reference spectra for the same spectral regionare entered into separate arrays. The data points of these arrays aredenoted V_(ji), where i=1,2, . . . m data points and j=1,2, . . . nconstituents). It is noted that in the Equations and text describingsame that follows, some symbols may be bolded and/or italicized atcertain locations but not at other locations, however this is not meantto alter the correlation or change the meaning of the symbol herein.

The method for fitting the measured sample plasma spectrum, P_(i) ^(o),with a linear combination of n constituent spectra is based on thepremise that there are a set of coefficients (weighting factors), c_(j),corresponding to the contributions of component j (lipoprotein subclasscomponents and protein component), and a coefficient, c_(p) ^(I),corresponding to the imaginary portion of the sample plasma spectrum,such that for each data point, P_(i) ^(o)≅P_(i) ^(c), where

$\begin{matrix}{P_{i}^{c} = {\left( {\sum\limits_{j = 1}^{n}{c_{j}V_{ji}}} \right) + {c_{p}^{I}{V_{i}^{I}\left( {{calculated}\mspace{14mu}{plasma}\mspace{14mu}{spectrum}} \right)}}}} & (1)\end{matrix}$In the past, the best fit was achieved when the root mean square error,

$\begin{matrix}\sqrt{\frac{1}{m - n}\left( {\sum \in_{i}^{2}} \right)} & (2)\end{matrix}$was minimized, where ε_(i)=P_(i) ^(o)−P_(i) ^(c). This was accomplishedby finding those coefficients which minimize Σε_(i) ², that is, when

$\begin{matrix}{{\frac{{\partial\sum} \in_{i}^{2}}{\partial c_{j}} = 0},} & (3)\end{matrix}$j=1,2, . . . n+1 (n−1 subspecies components plus protein and plasmaspectrum phase contributions). Differentiation results in n+1simultaneous linear equations:

$\begin{matrix}{{{{\sum\limits_{i = 1}^{m}{P_{i}^{o}V_{ki}}} = {\sum\limits_{j = 1}^{n + 1}{c_{j}\left( {\sum\limits_{j = 1}^{M}{V_{ki}V_{ji}}} \right)}}},{k = 1},2,{{\ldots\mspace{11mu} n} + 1}}{If}} & (4) \\{a_{kj} = {{\sum\limits_{j = 1}^{m}{V_{ki}V_{ji}\mspace{14mu}{and}\mspace{14mu} s_{k}}} = {\sum\limits_{i = 1}^{m}{P_{i}^{0}V_{ki}}}}} & (5)\end{matrix}$then there are n+1 simultaneous linear equations of the form:

$\begin{matrix}{{{\sum\limits_{j = 1}^{m}{c_{j}a_{kj}}} = s_{k}},{k = 1},2,{{\ldots\mspace{11mu} n} + 1}} & (6)\end{matrix}$Forming the n+1×n+1 matrix, [A]=[a_(k) _(j) ], j=1,2 . . . n+1; k=1,2 .. . n+1, gives [A]C=S, where C and S are the column vectors,

$\begin{matrix}{\begin{bmatrix}c_{1} \\c_{2} \\\vdots \\c_{n} \\c_{n + 1}\end{bmatrix}\mspace{14mu}{{and}\mspace{14mu}\begin{bmatrix}s_{1} \\s_{2} \\\vdots \\s_{n} \\s_{n + 1}\end{bmatrix}}} & (7)\end{matrix}$The coefficients providing the best fit were calculated by decompositionof the matrix [A] into a new set of m×m matrices known collectively asthe “singular value decomposition” of [A]:[A]=[U][W][V] ^(T)  (8)where [U] is a matrix of orthogonal column vectors (scalar products=0),[V]^(T) is the transpose of an orthogonal matrix [V], and [W] is adiagonal matrix with positive or zero elements, called “singularvalues:”

$\begin{matrix}{{\lbrack W\rbrack = \begin{bmatrix}w_{1} & 0 & \cdots & 0 \\0 & w_{2} & \cdots & 0 \\\vdots & \vdots & \ddots & \vdots \\0 & 0 & \cdots & w_{m}\end{bmatrix}}{{{From}\mspace{14mu}{this}},}} & (9) \\{{\lbrack A\rbrack^{- 1} = {{\lbrack V\rbrack\lbrack W\rbrack}^{- 1}\lbrack U\rbrack}^{T}}{where}} & (10) \\{\lbrack W\rbrack^{- 1} = \begin{bmatrix}{1/w_{1}} & 0 & \cdots & 0 \\0 & {1/w_{2}} & \cdots & 0 \\\vdots & \vdots & \ddots & \vdots \\0 & 0 & \cdots & {1/w_{m}}\end{bmatrix}} & (11)\end{matrix}$which allows C to be solved for:C=[V][W] ⁻¹ [U] ^(T) S  (12)where C was the best possible solution vector, provided that values ofw_(j) below a certain threshold value (selected by the user) are ignored(1/w_(j) set to zero). These singular values can give rise to“ill-conditioned” linear combinations of near degenerate solutions,being most corrupted by roundoff errors. The actual solution of C wasobtained by “back-substitution” in which w_(m) is determined, allowingfor the solution of w_(m-1), etc.

The root mean square deviation (RMSD) is computed as

$\begin{matrix}{{\,^{\sigma}{RMS}} = \sqrt{\frac{1}{m - n - 1}{\sum\limits_{i = 1}^{m}\left( {P_{i}^{o} - P_{i}^{c}} \right)^{2}}}} & (13)\end{matrix}$

The correlation coefficient was computed as

$\begin{matrix}{r^{p} = \frac{\sum\limits_{i = 1}^{m}{\left( {P_{i}^{o} - \left\langle P_{i}^{o} \right\rangle} \right)\left( {P_{i}^{c} - \left\langle P_{i}^{c} \right\rangle} \right)}}{\sqrt{\left( {\sum\limits_{i = 1}^{m}{\left( {P_{i}^{o} - \left\langle P_{i}^{o} \right\rangle} \right)^{2}{\sum\limits_{i = 1}^{m}\left( {P_{i}^{c} - \left\langle P_{i}^{c} \right\rangle} \right)^{2}}}} \right)}}} & (14)\end{matrix}$

In the past, the component coefficients resulting from this lineshapeanalysis provided the concentrations of the lipoprotein and proteinconstituents in each plasma sample. Each concentration can be expressedrelative to the concentration of the lipoprotein whose spectrum is usedas the reference. In operation, the final concentrations may benormalized to the integrated area of the resonance from atri-methylacetate external standard sample run on the same day tocorrect for variations in the detection sensitivity of the NMRspectrometer.

As described above, the least squares method used in the past forNMR-derived measurement of lipoprotein subclasses required that thederived concentrations be a positive value. Generally described, in thepast, when a negative coefficient for a selected constituent associatedwith one of the standards was encountered it was constrained to zero,and the calculation was performed again, subject to that constraint. Thelatter constraint can be desirable when fitting plasma samples that maynot contain one or more of the components included in the fit model orbecause experimental errors in the data (noise) can cause thecalculation to give negative values for concentrations for thesecomponents.

FIG. 5 illustrates a flow chart of operations with reference to certainof the above-stated equations in blocks 100-160. In operation, spectraof subspecies components are read into Array V (block 100). The realpart of the sample plasma spectrum is read into Array P⁰ (block 110).The imaginary part of the sample plasma spectrum is read into the ArrayV (block 120). Marix [A] and S vector are calculated (block 130) usingEquation 5. Matrix [A] is decomposed into a singular value decomposition(block 140) such as by using Equation 8. The singular values areselected based on a predetermined acceptance function (block 145). Thecoefficient vector C is calculated using back substitution (block 150).The negative values in C are sequentially set to zero and the curve isrefit, until there are no negatives left. The yes or no inquiry at(block 151) asks whether there are negatives left and, if so, directsthe program to return to the operation in (block 130) and, if not,directs the operations to advance to (block 155). C is multiplied bynormalization constants to obtain concentrations (block 155). The rootmean square deviation and correlation coefficient are calculated (block160) such as by using Equations 13 and 14.

Embodiments of the present invention modify and improve on theconventional protocol by employing operations that can reducemeasurement variability in individual constituents and/or by reducingthe number of constituents of interest that are reported as having a “0”value. The variability can be assessed by repeatedly analyzing a givensample and measuring the individual constituents. The individualconstituents measured by the present invention will typically beclustered more tightly together relative to the individual constituentsmeasured by the conventional protocol. The methods and systems canreduce the variability by at least about 50% relative to the priormethod for the same sample. Further, when analyzing the same sample inrepeated interrogations, the measured values of at least a majority ofthe constituents of interest, if not all of the constituents ofinterest, can be reproducible, typically within about +/−2.34% (medianCV).

Referring now to FIG. 6, operations of certain embodiments of theinvention are illustrated. It is noted that the term “matrix,” as usedherein, can, in certain embodiments, be a vector, as a vector is aspecial form of a matrix (i.e., a vector is a matrix with n rows and 1column, or 1 row and k columns). As shown in FIG. 6, the operations caninclude generating a mathematical design matrix of constituent datacomprising a plurality of mathematical constituent matrix data sets,each constituent data set including amplitude values of a respectivespectrum lineshape of a selected independent constituent parameter overdesired data points generated by a predetermined analysis method (of aknown reference sample) (block 200). The selected constituent parameter(the independent parameter) can be wavelength, voltage, current, speed,force, torque, pressure, movement, energy, chemical shift (ppm),temperature, and frequency. Exemplary dependent parameters of interestmay include, but are not limited to, intensity, opacity, transmittance,reflectance, fluorescence, vibration, or other desired parameter. Theconstituent data of the design matrix (“A”) can be reference or standarddata established a priori from separate individual analysis of discreteconstituents of interest and/or stored in an accessible database to beused as a standard and applied in analysis of all or selected ones ofunknown samples.

A composite mathematical matrix can be generated comprising a data setof amplitude values of a composite spectrum lineshape over the desireddata points for an unknown sample that is generated by a predeterminedanalysis method. The composite lineshape comprises spectralcontributions from a plurality of the selected individual constituents(block 205). The design matrix can be rotated to yield a rotated designmatrix of principal components (which may, in certain embodiments, bemathematically represented by matrix “Z” as will be discussed furtherbelow) and processed to selectively exclude data for certain principalcomponents to generate a reduced design matrix (which may, in certainembodiments, be represented mathematically by matrix “X*” as will bediscussed further below) (block 220). The term “principal components”means individual identifiable constituents (and may include bothrelevant and non-relevant constituents) in the rotated space. Inoperation, in certain embodiments, the operations can includemathematically rotating the design matrix, interrogating the rotateddesign matrix (using an acceptance function) to find those rotatedprincipal components with contributions that benefit the deconvolution,and rotating back those accepted principal components to form thereduced design matrix.

In certain embodiments, a normal equations matrix (which, in certainembodiments, may be mathematically represented by matrix “X^(T)X”) canbe computed from the design matrix (block 225). The normal equationsmatrix can be interrogated by applying a predetermined acceptancefunction (“A (λ)”) to the principal components to generate the reduceddesign matrix. The acceptance function can be a forced logic function of“0” and “1” (representative of rejected (excluded) values and accepted(included) values, respectively) or may be a relative or absolutefunction that discards the principal components having values low withrespect to other components or relative to a predefined threshold (i.e.,the values having the least significance) and retaining the moresignificant values in the reduced design matrix. The reduced matrix maybe generated by rotating the design matrix and eliminating the column orcolumns in the rotated design matrix with the most “0”s as determined bythe acceptance function.

Regression fit weighting coefficients can be computed based on acceptedprincipal components of the rotated design matrix in the reduced designmatrix to determine the presence of and/or measurement of the selectedor target constituents in the unknown sample undergoing analysis (block230). In particular embodiments, the weighting coefficients may bedetermined according to Equation (21) as will be discussed furtherbelow. A sequential least squares regression analysis can then beemployed to restrict or restrain negative coefficients to zero until all(or substantially all) constituents of interest are non-negative (block231). In certain embodiments, before the sequential regression analysisevaluation is performed, the reduced design matrix is combined with thecomposite matrix to define a first set of weighting factors.

Described differently, the signal from the unknown test sample can beprojected onto the space spanned by selected principal components andthe projection coefficients can be transformed back into the originalspace to provide a reduced design matrix for arriving at weightingcoefficients. As such, the design matrix can be mapped into the rotateddesign matrix and the components selected to yield the reduced designmatrix.

The reduced design matrix can be generated based on predeterminedcriteria using a shrinkage estimator. In certain embodiments, theshrinkage estimator can be based on the spectral decomposition of amatrix defined by the multiplication of the constituent matrix with thetransposed constituent matrix. In certain embodiments, the shrinkageestimator can be found by projecting the constituent matrix onto thespace spanned by the accepted basis set determined from the rotation ofthe design matrix, and shrinking the projection of the constituentmatrix on the orthogonal subspace to zero. A particularly suitableshrinkage estimator is described in Equation (21).

It is noted that other shrinkage estimators may also be employed.Generally stated, a shrinkage estimator of a parameter b is anyestimator B(X) of the data X such that ∥E{B(X)}∥<∥b∥. A simple examplewould be to take an unbiased estimator of b, say U(X), and multiply by aconstant smaller than 1: B(X)=pU(X) where 0<p<1. Because U(X) isunbiased, by definition of unbiased, E{U(X)}=b. Then the norm of theexpectation could be expressed as ∥E{B(X)}∥=∥E{pU(X)}∥=p∥E{U(X)}∥=p∥b∥<∥b∥since p<1. In the shrinkage estimator ofEquation (21), shrinkage is carried out selectively, in the direction ofzero for some components, and not for others.

The number of individual constituent data sets can be at least ten (10),each representing a respective one of at least ten (10) differentclosely correlated chemical constituents, some of the constituentshaving overlapping signal lines in a region of the spectrum analyzed(block 202). The number of columns in the design constituent matrix cancorrespond to the number of different individual constituents ofinterest, and, where needed, at least one additional column, which maybe a matrix of variables, representing spectra contributions from atleast one non-relevant variable constituent and/or noise (block 201). Inoperation, this additional column may not be used (i.e., “0”). The atleast one non-relevant variable can be a constituent known to be in thesample but not a target interest and/or background or environmentalnoise, and the like.

In certain embodiments, the predetermined analysis method is NMRspectroscopy, and the composite signal represents intensity over adesired interval or region in a chemical shift spectrum (typicallyrepresented in ppm) such that intensity is the dependent variableparameter (block 212).

FIG. 7 is a schematic illustration of certain embodiments of thedeconvolution operations used to evaluate closely correlated signaldata. As shown, a design matrix “X” of constituent data comprising aplurality of individual mathematical data sets, each constituent dataset including amplitude values of a respective spectrum lineshape of aselected constituent parameter over the variable space, spectrum length,or data points of interest, is obtained. The coordinate system of thedesign matrix is rotated to generate a rotated design matrix “Z” and,ultimately, a reduced design matrix “X*” (and a related transposedmatrix “X**”). The line extending between X* and X** represents aclassifier or acceptance function that determines what principalcomponent data in X will be excluded from X*. The matrix is then rotatedback to the original coordinate system, thereby generating a reduceddesign matrix “X*” with data from X modified by the analysis performedat the rotation of the coordinate system. The matrix of the compositespectrum lineshape data “Y” is projected onto X* and the weightingcoefficients “b” calculated. A sequential least squares (“SLS”)regression analysis is performed on the defined weighting coefficientsto ensure that positive weighting coefficients are established. Theoperations may be iteratively repeated.

FIG. 8 is a block diagram of exemplary embodiments of data processingsystems that illustrates systems, methods, and computer program productsin accordance with embodiments of the present invention. The processor310 communicates with the memory 314 via an address/data bus 348. Theprocessor 310 can be any commercially available or custommicroprocessor. The memory 314 is representative of the overallhierarchy of memory devices containing the software and data used toimplement the functionality of the data processing system 305. Thememory 314 can include, but is not limited to, the following types ofdevices: cache, ROM, PROM, EPROM, EEPROM, flash memory, SRAM, and DRAM.

As shown in FIG. 8, the memory 314 may include several categories ofsoftware and data used in the data processing system 305: the operatingsystem 352; the application programs 354; the input/output (I/O) devicedrivers 358; a Module of a Mathematical Model of HDL-related CHD Riskthat considers internal concentrations of small and large HDL particles350; and the data 356. The HDL Predictive Risk Module 350 can include amathematical model that employs a predetermined increased weightingfactor for at least one LDL particle subclass to provide a weighted riskHDL particle number that adjusts the measured values in the blood orplasma sample.

The data 356 may include signal (constituent and/or composite spectrumlineshape) data 362 which may be obtained from a data or signalacquisition system 320. As will be appreciated by those of skill in theart, the operating system 352 may be any operating system suitable foruse with a data processing system, such as OS/2, AIX or OS/390 fromInternational Business Machines Corporation, Armonk, N.Y., WindowsCE,WindowsNT, Windows95, Windows98, Windows2000 or WindowsXP from MicrosoftCorporation, Redmond, Wash., PalmOS from Palm, Inc., MacOS from AppleComputer, UNIX, FreeBSD, or Linux, proprietary operating systems ordedicated operating systems, for example, for embedded data processingsystems.

The I/O device drivers 358 typically include software routines accessedthrough the operating system 352 by the application programs 354 tocommunicate with devices such as I/O data port(s), data storage 356 andcertain memory 314 components and/or the image acquisition system 320.The application programs 354 are illustrative of the programs thatimplement the various features of the data processing system 305 and caninclude at least one application, which supports operations according toembodiments of the present invention. Finally, the data 356 representsthe static and dynamic data used by the application programs 354, theoperating system 352, the I/O device drivers 358, and other softwareprograms that may reside in the memory 314.

While the present invention is illustrated, for example, with referenceto the Module 350 being an application program in FIG. 8, as will beappreciated by those of skill in the art, other configurations may alsobe utilized while still benefiting from the teachings of the presentinvention. For example, the HDL risk Model Module 350 may also beincorporated into the operating system 352, the I/O device drivers 358or other such logical division of the data processing system 305. Thus,the present invention should not be construed as limited to theconfiguration of FIG. 8, which is intended to encompass anyconfiguration capable of carrying out the operations described herein.

In certain embodiments, the Module 350 includes computer program codefor providing a single risk predictor number for HDL and LDL particlesas well as a total single combined risk number using the ratio of theweighted HDL and LDL risk values in a subject that can indicate whethertherapy intervention is desired and/or track efficacy of a therapy usingthe risk predictor number as a sensitive reflection of what is occurringat the arterial wall.

For NMR derived lipoprotein measurements, the computer program code caninclude a sequential least squares regression analysis based on astatistical model comprising: (a) a mathematical composite matrixrepresenting spectrum measurements of the amplitude of a compositesignal of an unknown sample across “n” points in the spectrum; and (b) adesign matrix including respective mathematical matrices for theamplitude of each of a plurality of individual selected constituentsacross “n” points in the spectrum. The shrinkage estimator andacceptance function can be used to generate optimum weighting factors“b_(opt)” for each constituent of interest based on the differencebetween the composite signal amplitude and the constituent amplitudesdefined by interrogation of the values in the constituent and compositevectors. The analysis can be iteratively repeated in a sequential leastsquares regression model until target or selected constituents have beenassigned non-negative weighting factors such that a sequential leastsquares statistical evaluation produces a satisfactory non-negativesolution set for the target constituents.

The I/O data port can be used to transfer information between the dataprocessing system 305 and the image scanner or acquisition system 320 oranother computer system or a network (e.g., the Internet) or to otherdevices controlled by the processor. These components may beconventional components such as those used in many conventional dataprocessing systems, which may be configured in accordance with thepresent invention to operate as described herein.

While the present invention is illustrated, for example, with referenceto particular divisions of programs, functions and memories, the presentinvention should not be construed as limited to such logical divisions.Thus, the present invention should not be construed as limited to theconfiguration of FIG. 8 but is intended to encompass any configurationcapable of carrying out the operations described herein.

More particularly described, in particular embodiments, a target sampleto be analyzed may have a number of different selected parts orconstituents or individual or groupings of selected constituents. Thenumber of constituent parts may be noted as k. Thus, a sample undergoinganalysis can include constituent parts, P₁, . . . , P_(k). As notedabove, the number k may be at least 10, and can be between 35-40 or evenlarger. The sample can be analyzed on a desired suitable analyticalinstrument, with the amplitude of the independent variable (e.g.,intensity, wavelength, retention time, current, etc., as describedabove) varied. The amplitude or value of the independent constituent(s)varies corresponding to the detector response of the analyticalinstrument and the variation can be recorded in the form of a spectrum.The spectrum or lineshape consists of amplitude measurements (that maybe intensity measurements in certain embodiments) at n points. Theseamplitude measurements of the sample being analyzed are stored in acomposite matrix, Y.

Also, each constituent part, P_(j), j=1 to k, is separately analyzed todefine a standard or reference over the same independent variable space,region, or data points as the sample undergoing analysis. Each set ofthe respective reference constituent spectral amplitudes (such asintensities) are stored in a matrix X_(j), where, j=1, . . . , k, alsoof length n. Thus, a design constituent matrix X can be represented by:X=[X ₁ ,X ₂ . . . ,X _(k) , Z]  (15)where Z is a matrix of amplitude data regarding at least one additionalvariable that can be deconvolved from the spectral signal. For example,Z may contain data representing spectral intensities of other known orunknown constituents, the imaginary part of the spectrum of the analytesample, (where Y contains the real part of the spectrum), noise, etc. .. . However, it is noted that Z can be a matrix, a vector, or, incertain embodiments, even null (a degenerate form of matrix with 0columns).

In certain embodiments, Z is a matrix of size n×w, where w≧0. In certainparticular embodiments, w=1. The estimated contributions of theindividual components to the sample or analyte composite spectrum can befound by determining a normalized or optimal coefficient weightingsb_(opt) given by equation 16. The normalized weighting coefficientminimizes the values inside the brackets of the arg min_(b) function.b _(opt) =arg min_(b) {∥Y−Xb∥: b≧0}.  (16)These normalized weightings can be found by solving equation (16) usinga shrinkage estimator to the regression problem, followed by theapplication of non-negative least squares to ensure that thenon-negativity constraint is satisfied. The cycle is repeated until theleast squares solution provides only non-negative weighting factors. Theshrinkage estimator can be based on the spectral decomposition of thematrix M=X^(T)X where X^(T) represents a transposition of theconstituent matrix X. Further, the spectral decomposition matrix M maybe expressed by the following:M=QΛQ^(T)  (17)where Q (k+w)×(k+w) is orthogonal, and Λ (k+w)×(k+w) is a diagonalmatrix comprising eigenvalues. The eigenvalue matrix Λ is sorted withthe largest eigenvalue in the (1,1) element or position, the nextlargest value in the (2,2) element or position, and continuing left toright and top to bottom, etc. . . . , until the smallest element isplaced in the (n, n) element or position. An adjustable toleranceparameter “τ” can be defined such that τ≧0. Also an acceptance orclassifier function “A” can be defined such that A(λ):

→{0, 1} which indicates which component is accepted into a fittingmodel.

A reduced eigenvalue matrix “Λ_(red)” can be defined as:Λ_(red)=Λdiag(A(Λ_(j,j)))  (18)The X* matrix (“reduced design matrix”) described above may beidentified as:X*=QΛ _(red) ^(1/2)  (19)One acceptance function that has been used is:

$\begin{matrix}{{A(\lambda)} = \left\{ \begin{matrix}1 & {{{if}\mspace{14mu}\lambda} > {\tau\;\Lambda_{1,1}}} \\0 & {otherwise}\end{matrix} \right.} & (20)\end{matrix}$where τ has been chosen to minimize Var b while maintaining E{b}.Examples of values for τ are in the range between 10⁻⁶ and 4×10⁻⁶ forcases where k is about 37, i.e., where there are about 37 constituentsor parts “P₁—P₃₇”. Other values may be appropriate for lesser or greaternumbers of constituents. Then b can be calculated as:b=QΛ _(red) ⁻¹ Q ^(T) X ^(T) Y  (21)Configuration of Exemplary System for Acquiring and Calculating AdjustedHDL and/or LDL Particle Subclass Concentration Measurements and/or HDLand LDL Particle Risk Numbers

Referring now to FIG. 9, a system 7 for acquiring and calculating thelineshape of a selected sample is illustrated. The system 7 includes anNMR spectrometer 10 for taking NMR measurements of a sample. In oneembodiment, the spectrometer 10 is configured so that the NMRmeasurements are conducted at 400 MHz for proton signals, in otherembodiments the measurements may be carried out at 360 MHz or othersuitable frequency. Other frequencies corresponding to a desiredoperational magnetic field strength may also be employed. Typically, aproton flow probe is installed, as is a temperature controller tomaintain the sample temperature at 47+/−0.2 degrees C. Field homogeneityof the spectrometer 10 can be optimized by shimming on a sample of 99.8%D₂O until the spectral linewidth of the HDO NMR signal is less than 0.6Hz. The 90° RF excitation pulse width used for the D₂O measurement istypically ca. 6-7 microseconds.

Referring again to FIG. 9, the spectrometer 10 is controlled by adigital computer 11 or other signal processing unit. The computer 11should be capable of performing rapid Fourier transformations and mayinclude for this purpose a hard-wired sine table and hardwired multiplyand divide circuit. It may also include a data link 12 to an externalpersonal computer 13, and a direct-memory-access channel 14 whichconnects to a hard disc unit 15.

The digital computer 11 may also include a set of analog-to-digitalconverters, digital-to-analog converters and slow device I/O ports whichconnect through a pulse control and interface circuit 16 to theoperating elements of the spectrometer. These elements include an RFtransmitter 17 which produces an RF excitation pulse of the duration,frequency and magnitude directed by the digital computer 11, and an RFpower amplifier 18 which amplifies the pulse and couples it to the RFtransmit coil 19 that surrounds sample cell 20. The NMR signal producedby the excited sample in the presence of a 9.4 Tesla polarizing magneticfield produced by superconducting magnet 21 is received by a coil 22 andapplied to an RF receiver 23. The amplified and filtered NMR signal isdemodulated at 24 and the resulting quadrature signals are applied tothe interface circuit 16 where they are digitized and input through thedigital computer 11 to a file in the disc storage 15. The module 350(FIG. 8) can be located in the digital computer 11 and/or in a secondarycomputer that may be on-site or remote. Additional automated clinicalNMR analyzer systems suitable for analyzing biospecimen are described inco-pending, co-assigned U.S. patent application Ser. No. 11/093,596, thecontents of which are hereby incorporated by reference as if recited infull herein.

After the NMR data are acquired from the sample in the measurement cell20, processing by the computer 11 produces another file that can, asdesired, be stored in the disc storage 15. This second file is a digitalrepresentation of the chemical shift spectrum and it is subsequentlyread out to the computer 13 for storage in its disc storage 25. Underthe direction of a program stored in its memory, the computer 13, whichmay be personal, laptop, desktop, or other computer, processes thechemical shift spectrum in accordance with the teachings of the presentinvention to print a report, which is output to a printer 26 orelectronically stored and relayed to a desired email address or URL.Those skilled in this art will recognize that other output devices, suchas a computer display screen, may also be employed for the display ofresults.

It should be apparent to those skilled in the art that the functionsperformed by the computer 13 and its separate disc storage 25 may alsobe incorporated into the functions performed by the spectrometer'sdigital computer 11. In such case, the printer 26 may be connecteddirectly to the digital computer 11. Other interfaces and output devicesmay also be employed, as are well-known to those skilled in this art.

The invention will now be described in more detail in the followingnon-limiting examples.

EXAMPLES

Exemplary weighting factors for LDL subclasses (Examples 1 and 2) andHDL subclasses (Example 3) were calculated using data from differentstudies with different coronary disease outcomes. Example 1 uses data onrelations of large and small LDL particle numbers with carotidatherosclerosis as assessed on a per particle basis. Example 2 uses dataon relations of IDL, large LDL, and small LDL particle numbers withincident CHD events (nonfatal myocardial infarction and CHD death) asassessed on a per particle basis. Example 3 uses data on relations ofsmall, large and medium HDL particle numbers with carotidatherosclerosis as assessed on a per particle basis.

Example 1

Shown in the table are the relations of large and small LDL particlenumbers with carotid atherosclerosis as assessed on a per particlebasis. TABLE 2 is an example of LDL subclass weights using datapresented in Mora et al., Both Large and Small LDL ParticleConcentrations are Independently Associated with Carotid Atherosclerosisin the Multi-Ethnic Study of Atherosclerosis (MESA),

Abstract presented at 2005 Scientific Sessions of the American HeartAssociation, Dallas, Tex., Circulation. 2005; 112: II-802.

TABLE 2 Exemplary LDL subclass weighting for Carotid Atherosclerosis ΔIMT (per 100 nmol/L) LDL subclass weighting Large LDL-P 17.5 microns 1.5Small LDL-P 11.8 microns 1.0 Data are from a linear regression modelincluding both large and small LDL-P, adjusted for age, race, sex,hypertension, and smoking.

Shown in TABLE 2 are exemplary relations of large and small LDL particlenumbers with carotid atherosclerosis as assessed on a per particle basisbased on the MESA study. Subjects were 5,354 apparently healthyindividuals enrolled in the Multi-Ethnic Study of Atherosclerosis (MESA)who were not taking lipid-lowering medication. Data show the change(increase) in carotid intima-media thickness (IMT) per 100 nmol/Lincrement in the concentration of large and small LDL-P. The ratio of ΔIMT for large LDL-P/small LDL-P is about 1.5, which gives a weightingfactor for large LDL-P relative to small LDL-P.

It is contemplated that a third LDL subclass (IDL) can be included inthe risk model and a different LDL subclass weighting can be used forthe IDL contribution. IDL-P may have a weighting factor, which may behigher than the large LDL-P weight, such as between about 5-6 relativeto small LDL-P.

Example 2

Shown in TABLE 3 are the relationships of IDL and large and small LDLparticle numbers with incident CHD events (nonfatal myocardialinfarction and CHD death) as assessed on a per particle basis. The datawas derived from results presented in Otvos et al., Low-DensityLipoprotein and High-Density Lipoprotein Particle Subclasses PredictCoronary Events and Are Favorably Changed by Gemfibrozil Therapy in theVeterans Affairs High-Density Lipoprotein Intervention Trial,Circulation. 2006; 113; 1556-1563; originally published online Mar. 13,2006.

TABLE 3 Exemplary LDL subclass weighting for incident CHD Events Betacoefficient LDL Odds Ratio (per subclass 1 SD (per 1 SD) 100 nmol/L)weighting IDL-P  28 nmol/L 1.13 0.436 5.7 Large LDL-P 250 nmol/L 1.340.117 1.5 Small LDL-P 450 nmol/L 1.41 0.076 1.0 Data are from a logisticregression model including LDL and HDL subclasses in the same model,adjusted for treatment group, age, hypertension, smoking, body massindex, and diabetes.

Subjects were men (364 cases, 697 controls) with existing coronarydisease enrolled in the Veterans Affairs HDL Intervention Trial(VA-HIT). Data show the odds ratios for a new CHD event associated witha 1 SD increment in the on-trial concentration of each LDL subclass. Thecorresponding beta coefficients show the relationships of each subclassto CHD events on a per particle basis. The ratios of the betacoefficients for IDL-P and large LDL-P relative to small LDL-P give theweighting factors for IDL-P and large LDL-P relative to small LDL-P.

The beta coefficients can be calculated according to the mathematicalexpression:beta coeff=ln OR/1SD(100).

Example 3

Table 4 below contains data from MESA relating the different HDLsubclasses to carotid IMT and shows the resulting weighting factors. TheHDL risk factor can be provided as a discrete risk factor and can beused to generate a parameter called R_(HDL) or Risk _(HDL) (or suitableidentifier) to be calculated like R_(LDL). The R_(HDL) could also becalled the “Good Particle Index” and the R_(LDL) could be called the“Bad Particle Index”. A combined parameter (called the LipoproteinParticle Index or the like) using the ratio of Bad/Good Particle Indexescould be reported similar to the TC/HDL-C or LDL-C/HDL-C ratio.

TABLE 4 Exemplary HDL subclass weights Δ IMT HDL Δ IMT (per subclass 1SD (per 1 SD) 1 μmol/L) weighting Large HDL-P 4.0 μmol/L −25.1 microns−6.3 microns 2.2 Medium 4.2 μmol/L −16.6 microns −3.9 microns 1.4 HDL-PSmall HDL-P 4.6 μmol/L −13.2 microns −2.9 microns 1.0

It is contemplated that additional studies may further optimize theweighting factors.

The foregoing is illustrative of the present invention and is not to beconstrued as limiting thereof. Although a few exemplary embodiments ofthis invention have been described, those skilled in the art willreadily appreciate that many modifications are possible in the exemplaryembodiments without materially departing from the novel teachings andadvantages of this invention. Accordingly, all such modifications areintended to be included within the scope of this invention as defined inthe claims. In the claims, means-plus-function clauses, where used, areintended to cover the structures described herein as performing therecited function and not only structural equivalents but also equivalentstructures. Therefore, it is to be understood that the foregoing isillustrative of the present invention and is not to be construed aslimited to the specific embodiments disclosed, and that modifications tothe disclosed embodiments, as well as other embodiments, are intended tobe included within the scope of the appended claims. The invention isdefined by the following claims, with equivalents of the claims to beincluded therein.

That which is claimed is:
 1. A method of determining a subject's risk ofhaving and/or developing coronary heart disease, comprising: conductingNuclear Magnetic Resonance to obtain concentration measurements of smalland large High Density Lipoprotein subclass particles from a single invitro blood plasma or serum sample of a subject; and programmaticallynumerically adjusting at least one of the obtained small and large HighDensity Lipoprotein subclass particle concentration measurements of thesingle in vitro blood plasma and/or serum sample based on apredetermined mathematical model with at least one weighting factor thatis multiplied to increase or decrease the small and/or large HighDensity Lipoprotein subclass particle concentration measurements,wherein the at least one weighting factor reflects predicted CHD riskfor the weighted High Density Lipoprotein subclass, to yield a HighDensity Lipoprotein risk number that can be greater than a summation ofan actual, unadjusted total High Density Lipoprotein particleconcentration measurement, to thereby determine the subject's HighDensity Lipoprotein based risk of coronary heart disease.
 2. A methodaccording to claim 1, further comprising conducting Nuclear MagneticResonance to obtain concentration measurements of medium High DensityLipoprotein particles in the sample, and wherein the adjusting step iscarried out to electronically adjust at least two of the small subclassparticle measurements, the large High Density Lipoprotein subclassparticle measurement and the medium High Density Lipoprotein particlemeasurement and calculating the High Density Lipoprotein risk numberbased on the at least two adjusted particle concentration measurements.3. The method of claim 1, further comprising electronically generating areport with actual High Density Lipoprotein particle subclassconcentrations and the High Density Lipoprotein risk number.
 4. A methodfor determining a subject's risk of coronary heart disease, comprising:conducting Nuclear Magnetic Resonance to obtain concentrationmeasurements of small and large High Density Lipoprotein subclassparticles in a biosample of interest; multiplying a weighting factor byat least one of the measured large and small High Density Lipoproteinparticle concentrations; and calculating a High Density Lipoprotein risknumber using the weighted High Density Lipoprotein particleconcentration(s), wherein the High Density Lipoprotein risk number is adiscrete parameter separate from actual High Density Lipoproteinsubclass particle concentrations and that is different from a summationof the obtained actual small and large High Density Lipoprotein particleconcentration measurements, wherein the obtaining, multiplying andcalculating steps are carried out using at least one processor.
 5. Amethod according to claim 4, wherein the High Density Lipoprotein risknumber comprises a weighted large High Density Lipoprotein particleconcentration summed with an actual unadjusted small High DensityLipoprotein particle concentration.
 6. A method according to claim 5,wherein the obtaining step comprises: deconvolving at least one NuclearMagnetic Resonance spectroscopic signal of the biosample to calculatethe small and large High Density Lipoprotein particle subclassconcentration measurements.
 7. A method according to claim 6, whereinthe obtaining step obtains a concentration measure of medium HighDensity Lipoprotein particles, and wherein the multiplying at least oneweighting factor step is carried out to increase the medium High DensityLipoprotein particle measurement and the large High Density Lipoproteinparticle measurement relative to the small High Density Lipoproteinparticle measurement.
 8. A method according to claim 7, wherein thebiosample is an in vitro blood plasma and/or serum sample, and whereinthe multiplying step is carried out to increase the medium High DensityLipoprotein particle measurement more than the large High DensityLipoprotein particle measurement.
 9. A method according to claim 4,wherein the multiplying step is carried out to apply an increased weightto the large High Density Lipoprotein subclass concentration measurementrelative to the small High Density Lipoprotein subclass concentration tocalculate the High Density Lipoprotein risk number.
 10. A computerprogram product for adjusting measured in vitro concentrations of HighDensity Lipoprotein particles to assess coronary heart disease risk, thecomputer program product comprising: a non-transitory computer readablestorage medium having embodied in said medium computer readable programcode that: (i) deconvolutes at least one Nuclear Magnetic Resonancesignal of a patient biosample into measured concentrations of small andlarge High Density Lipoprotein particle subclasses; and (ii) adjustsconcentration measurements of at least one of small and large HighDensity Lipoprotein particle subclasses of a biosample to generate aHigh Density Lipoprotein risk number to reflect a subject's risk ofhaving or developing coronary heart disease, wherein the High DensityLipoprotein risk number is calculated based on a defined mathematicalmodel to yield a numerical High Density Lipoprotein risk number that isdifferent from a summation of the obtained actual small and large HighDensity Lipoprotein particle concentration measurements.
 11. A computerprogram product according to claim 10, wherein the mathematical model isconfigured to increase the measured large High Density Lipoproteinparticle concentration measurement relative to the small High DensityLipoprotein particle concentration to define the High DensityLipoprotein risk number.
 12. A computer program product according toclaim 11, wherein the mathematical model is configured to multiply afirst weighting factor by the concentration measurement of small HighDensity Lipoprotein particles and multiply a second different weightingfactor by the concentration measurement of large High DensityLipoprotein particles and sum the weighted small and large High DensityLipoprotein particles to generate the High Density Lipoprotein risknumber.
 13. A computer program product according to claim 12, whereinthe computer readable program code of the mathematical model isconfigured to increase the actual large High Density Lipoproteinparticle concentration measurement relative to the actual small HighDensity Lipoprotein particle concentration measurement.
 14. A computerprogram product according to claim 10, wherein the computer readableprogram code that adjusts at least one of the measured small and largeHigh Density Lipoprotein particle concentrations is configured to adjustat least two of small High Density Lipoprotein particle concentration,large High Density Lipoprotein particle concentration and medium HighDensity Lipoprotein particle concentration such that both the large HighDensity Lipoprotein and the medium High Density Lipoprotein particlemeasurements are increased relative to the small High DensityLipoprotein subclass measurement to generate the High DensityLipoprotein risk number.
 15. A computer program product according toclaim 14, further comprising computer readable program code that definesthe High Density Lipoprotein risk number using a summation of theadjusted small and/or large High Density Lipoprotein particleconcentrations and scales the summation to thereby define acorresponding High Density Lipoprotein-based coronary heart diseaserisk.
 16. A computer program product according to claim 10, furthercomprising computer readable program code that calculates the HighDensity Lipoprotein particle risk number in units of concentration. 17.A computer program product according to claim 10, wherein the computerreadable program code that adjusts the High Density Lipoprotein particlemeasurement(s) is configured to generate different weighting factors forsmall and large High Density Lipoprotein particles to predict coronaryheart disease risk.
 18. A computer program product according to claim10, wherein the computer readable program code is configured to increasethe large High Density Lipoprotein particle measurement relative to thesmall High Density Lipoprotein particle measurement to adjust the actualmeasurements from the in vitro blood plasma or serum sample.
 19. Asystem for obtaining data regarding lipoprotein constituents in asubject, comprising: a Nuclear Magnetic Resonance spectrometer foracquiring at least one Nuclear Magnetic Resonance spectrum of an invitro blood, plasma or serum sample from a subject; and at least oneprocessor in communication with the Nuclear Magnetic Resonancespectrometer, the at least one processor configured to: determineconcentrations of small and large High Density Lipoprotein particlesubclasses in the sample; and multiply at least one weighting factor byat least one of the determined small and large High Density Lipoproteinparticle concentrations of the sample and sum weighted and unweightedconcentrations to yield a numerical High Density Lipoprotein risk numberfor the sample that is different from a summation of the obtained actualsmall and large High Density Lipoprotein particle concentrationmeasurements to determine a subject's risk of developing or havingcoronary heart disease.
 20. A system according to claim 19, wherein theat least one Nuclear Magnetic Resonance spectrum is a compositespectrum, the processor further configured to execute computer programcode defining a plurality of individual Nuclear Magnetic Resonanceconstituent spectra, each associated with a selected referencelipoprotein constituent signal lineshape, each constituent spectrumhaving associated spectra that contribute to the composite NuclearMagnetic Resonance spectrum of the blood plasma or serum sample.
 21. Asystem according to claim 19, wherein the at least one processor isconfigured to increase the actual measured large High DensityLipoprotein particle concentration relative to the actual small HighDensity Lipoprotein particle concentration of the respective singlesamples.
 22. A system according to claim 21, wherein the at least oneprocessor is configured to define a first weighting factor that ismultiplied by the small High Density Lipoprotein particle concentrationand a second different weighting factor that is multiplied by the largeHigh Density Lipoprotein particle concentration, wherein the secondweighting factor is larger than the first weighting factor.
 23. A systemaccording to claim 19, wherein the at least one processor is configuredto multiply at least one weighting factor to at least one of thedetermined small and large High Density Lipoprotein particleconcentrations of the sample to increase the measured concentration ofmedium High Density Lipoprotein particles and the measured concentrationof large High Density Lipoprotein particles but not to the small HighDensity Lipoprotein particle measurement to determine coronary heartdisease risk.
 24. A system according to claim 23, wherein the at leastone processor is configured to generate a High Density Lipoprotein risknumber using the adjusted High Density Lipoprotein particleconcentrations.
 25. A method of evaluating a person's risk of having ordeveloping coronary heart disease, comprising: electronicallycalculating a High Density Lipoprotein risk number for a person using asummation of at least two of small, medium and large High DensityLipoprotein particle concentration values of a biosample from theperson, wherein at least one of the concentration values for thesummation for the sample is electronically increased, using a processor,relative to the measured small High Density Lipoprotein particleconcentration value using a predefined weighting factor, wherein theHigh Density Lipoprotein risk number can have a concentration value thatis greater than a total actual High Density Lipoprotein subclassconcentration measurement.
 26. A method according to claim 25, furthercomprising electronically, using a processor, defining a risk ofdeveloping or having coronary heart disease associated with the HighDensity Lipoprotein risk number.
 27. A method according to claim 25,further comprising using a processor to mathematically scale thecalculated risk number.
 28. A method according to claim 25, furthercomprising using a processor to increase both the medium High DensityLipoprotein and large High Density Lipoprotein particle concentrationvalues relative to actual small High Density Lipoprotein particleconcentration value and summing these increased values with the actualsmall High Density Lipoprotein particle concentration measurement valueto generate the High Density Lipoprotein risk number.
 29. A methodaccording to claim 28, further comprising using a processor to calculatea Low Density Lipoprotein risk number using at least two of small,medium and large Low Density Lipoprotein particle concentration values,wherein at least one of the concentration values is increased using adefined weighting factor relative to the measured small Low DensityLipoprotein particle concentration value.
 30. A method according toclaim 29, further comprising using a processor to calculate alipoprotein particle index from a ratio of the Low Density Lipoproteinrisk number to the High Density Lipoprotein risk number.
 31. A methodaccording to claim 30, further comprising using a processor to generatea patient-specific report presenting the lipoprotein particle index, theLow Density Lipoprotein risk number and the High Density Lipoproteinrisk number.
 32. A method according to claim 25, wherein the calculatingstep is carried out using at least one processor.
 33. A method ofassessing coronary heart disease risk in a patient, comprising:generating a single risk predictor variable using a ratio of a LowDensity Lipoprotein particle number as a numerator and a High DensityLipoprotein particle number as a denominator, wherein the generatingstep is carried out using at least one processor, and wherein the LowDensity Lipoprotein and High Density Lipoprotein particle numbers arecalculated using at least one predefined weighting factor that ismultiplied by an actual concentration measurement of at least onesubclass of respective Low Density Lipoprotein and High DensityLipoprotein particle subclasses, wherein the multiplied concentration ofat least one subclass of respective Low Density Lipoprotein and HighDensity Lipoprotein particle subclasses increases or decreases actualconcentrations of the Low Density Lipoprotein particle number and theHigh Density Lipoprotein particle number and are different from asummation of total Low Density Lipoprotein particle number and totalHigh Density Lipoprotein particle number.